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    Conversation Mining in Multi-agent Systems

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    International audienceThe complexity of Multi-Agent Systems is constantly increasing. With the growth of the number of agents, interactions between them draw complexan d huge conversations, i.e. sequences of messages exchanged inside the system. In this paper, we present a knowledge discovery process, mining those conversations to infer their underlying models, using stochastic grammatical inference techniques. We present some experiments that show the process we design is a good candidate to observe the interactions between the agents and infer the conversation models they build together
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